Process parameters selection for friction surfacing applications using intelligent decision support

Abstract

Friction surfacing is an advanced manufacturing process, which has been successfully developed and commercialised over the past decade. The process is used for corrosion and wear resistant coatings and for reclamation of worn engineering components. At present the selection of process parameters for new coating materials or substrate geometries is by experiment requiring lengthy development work. The major requirement is for flexibility to enable rapid changes of process parameters in order to develop new applications, with variations of materials and geometries in a cost effective and reliable manner. Further improvement requires development of appropriate mathematical models of the process, which will facilitate the introduction of optimisation techniques for efficient experimental work as well as the introduction of real-time feedback adaptive control. This paper considers the use of combined artificial intelligence and modelling techniques. It includes a new frame of a neurofuzzy-model based decision support system—FricExpert, which is aimed at speeding up the parameter selection process and to assist in obtaining values for cost effective development. Derived models can then be readily used for optimisation techniques, discussed in our earlier work